Overview

Brought to you by YData

Dataset statistics

Number of variables12
Number of observations1528
Missing cells0
Missing cells (%)0.0%
Duplicate rows204
Duplicate rows (%)13.4%
Total size in memory155.2 KiB
Average record size in memory104.0 B

Variable types

Numeric12

Alerts

Dataset has 204 (13.4%) duplicate rowsDuplicates
citric acid is highly overall correlated with fixed acidity and 2 other fieldsHigh correlation
density is highly overall correlated with fixed acidityHigh correlation
fixed acidity is highly overall correlated with citric acid and 2 other fieldsHigh correlation
free sulfur dioxide is highly overall correlated with total sulfur dioxideHigh correlation
pH is highly overall correlated with citric acid and 1 other fieldsHigh correlation
total sulfur dioxide is highly overall correlated with free sulfur dioxideHigh correlation
volatile acidity is highly overall correlated with citric acidHigh correlation
citric acid has 130 (8.5%) zerosZeros

Reproduction

Analysis started2024-11-04 21:05:58.289384
Analysis finished2024-11-04 21:06:52.072251
Duration53.78 seconds
Software versionydata-profiling vv4.9.0
Download configurationconfig.json

Variables

fixed acidity
Real number (ℝ)

HIGH CORRELATION 

Distinct86
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.1712696
Minimum4.6
Maximum15.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.9 KiB
2024-11-04T18:06:52.296011image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum4.6
5-th percentile6.1
Q17.1
median7.8
Q39
95-th percentile11.3
Maximum15.9
Range11.3
Interquartile range (IQR)1.9

Descriptive statistics

Standard deviation1.5686451
Coefficient of variation (CV)0.19197079
Kurtosis0.62203093
Mean8.1712696
Median Absolute Deviation (MAD)0.9
Skewness0.79997273
Sum12485.7
Variance2.4606475
MonotonicityNot monotonic
2024-11-04T18:06:52.627762image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.2 67
 
4.4%
7.1 57
 
3.7%
7.8 53
 
3.5%
7.5 52
 
3.4%
7 50
 
3.3%
7.7 49
 
3.2%
7.6 46
 
3.0%
6.8 46
 
3.0%
8.2 44
 
2.9%
7.4 43
 
2.8%
Other values (76) 1021
66.8%
ValueCountFrequency (%)
4.6 1
 
0.1%
4.7 1
 
0.1%
4.9 1
 
0.1%
5 6
0.4%
5.1 4
 
0.3%
5.2 6
0.4%
5.3 3
 
0.2%
5.4 5
 
0.3%
5.5 1
 
0.1%
5.6 14
0.9%
ValueCountFrequency (%)
15.9 1
 
0.1%
13.8 1
 
0.1%
13.3 1
 
0.1%
13 1
 
0.1%
12.8 3
0.2%
12.7 4
0.3%
12.6 4
0.3%
12.5 7
0.5%
12.4 3
0.2%
12.3 1
 
0.1%

volatile acidity
Real number (ℝ)

HIGH CORRELATION 

Distinct142
Distinct (%)9.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.52899869
Minimum0.12
Maximum1.58
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.9 KiB
2024-11-04T18:06:52.926170image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.12
5-th percentile0.27
Q10.39
median0.52
Q30.64
95-th percentile0.84
Maximum1.58
Range1.46
Interquartile range (IQR)0.25

Descriptive statistics

Standard deviation0.18036784
Coefficient of variation (CV)0.34096085
Kurtosis1.2175933
Mean0.52899869
Median Absolute Deviation (MAD)0.12
Skewness0.66953995
Sum808.31
Variance0.032532559
MonotonicityNot monotonic
2024-11-04T18:06:53.335205image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6 47
 
3.1%
0.43 42
 
2.7%
0.5 40
 
2.6%
0.36 38
 
2.5%
0.4 37
 
2.4%
0.58 37
 
2.4%
0.59 36
 
2.4%
0.56 34
 
2.2%
0.52 33
 
2.2%
0.39 32
 
2.1%
Other values (132) 1152
75.4%
ValueCountFrequency (%)
0.12 3
 
0.2%
0.16 2
 
0.1%
0.18 10
0.7%
0.19 2
 
0.1%
0.2 3
 
0.2%
0.21 4
 
0.3%
0.22 6
0.4%
0.23 5
 
0.3%
0.24 13
0.9%
0.25 7
0.5%
ValueCountFrequency (%)
1.58 1
 
0.1%
1.33 2
0.1%
1.24 1
 
0.1%
1.185 1
 
0.1%
1.18 1
 
0.1%
1.13 1
 
0.1%
1.115 1
 
0.1%
1.09 1
 
0.1%
1.07 1
 
0.1%
1.04 3
0.2%

citric acid
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct78
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2627356
Minimum0
Maximum1
Zeros130
Zeros (%)8.5%
Negative0
Negative (%)0.0%
Memory size23.9 KiB
2024-11-04T18:06:54.639693image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.09
median0.25
Q30.41
95-th percentile0.59
Maximum1
Range1
Interquartile range (IQR)0.32

Descriptive statistics

Standard deviation0.19125745
Coefficient of variation (CV)0.72794647
Kurtosis-0.73918329
Mean0.2627356
Median Absolute Deviation (MAD)0.16
Skewness0.35099701
Sum401.46
Variance0.036579414
MonotonicityNot monotonic
2024-11-04T18:06:54.974603image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 130
 
8.5%
0.49 54
 
3.5%
0.24 50
 
3.3%
0.02 49
 
3.2%
0.26 38
 
2.5%
0.1 35
 
2.3%
0.01 33
 
2.2%
0.08 33
 
2.2%
0.21 32
 
2.1%
0.32 32
 
2.1%
Other values (68) 1042
68.2%
ValueCountFrequency (%)
0 130
8.5%
0.01 33
 
2.2%
0.02 49
 
3.2%
0.03 30
 
2.0%
0.04 29
 
1.9%
0.05 20
 
1.3%
0.06 24
 
1.6%
0.07 20
 
1.3%
0.08 33
 
2.2%
0.09 30
 
2.0%
ValueCountFrequency (%)
1 1
 
0.1%
0.78 1
 
0.1%
0.76 1
 
0.1%
0.75 1
 
0.1%
0.74 3
0.2%
0.73 3
0.2%
0.72 1
 
0.1%
0.71 1
 
0.1%
0.7 2
0.1%
0.69 4
0.3%

residual sugar
Real number (ℝ)

Distinct86
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4559882
Minimum0.9
Maximum13.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.9 KiB
2024-11-04T18:06:55.309292image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.9
5-th percentile1.5
Q11.9
median2.2
Q32.5
95-th percentile4.6
Maximum13.9
Range13
Interquartile range (IQR)0.6

Descriptive statistics

Standard deviation1.1849188
Coefficient of variation (CV)0.48246111
Kurtosis22.244522
Mean2.4559882
Median Absolute Deviation (MAD)0.3
Skewness3.9434375
Sum3752.75
Variance1.4040326
MonotonicityNot monotonic
2024-11-04T18:06:55.617715image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 156
 
10.2%
1.8 128
 
8.4%
2.1 128
 
8.4%
2.2 122
 
8.0%
1.9 116
 
7.6%
2.3 107
 
7.0%
2.4 86
 
5.6%
2.5 82
 
5.4%
2.6 77
 
5.0%
1.7 76
 
5.0%
Other values (76) 450
29.5%
ValueCountFrequency (%)
0.9 2
 
0.1%
1.2 8
 
0.5%
1.3 5
 
0.3%
1.4 33
 
2.2%
1.5 30
 
2.0%
1.6 58
3.8%
1.65 2
 
0.1%
1.7 76
5.0%
1.75 2
 
0.1%
1.8 128
8.4%
ValueCountFrequency (%)
13.9 1
 
0.1%
13.4 1
 
0.1%
12.9 1
 
0.1%
10.7 1
 
0.1%
9 1
 
0.1%
8.9 1
 
0.1%
8.8 2
0.1%
8.6 1
 
0.1%
8.3 3
0.2%
8.1 2
0.1%

chlorides
Real number (ℝ)

Distinct152
Distinct (%)9.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.087124346
Minimum0.012
Maximum0.611
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.9 KiB
2024-11-04T18:06:55.977704image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.012
5-th percentile0.05335
Q10.07
median0.079
Q30.09
95-th percentile0.12565
Maximum0.611
Range0.599
Interquartile range (IQR)0.02

Descriptive statistics

Standard deviation0.047637908
Coefficient of variation (CV)0.54678067
Kurtosis41.641523
Mean0.087124346
Median Absolute Deviation (MAD)0.01
Skewness5.709874
Sum133.126
Variance0.0022693703
MonotonicityNot monotonic
2024-11-04T18:06:56.306906image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.08 65
 
4.3%
0.074 53
 
3.5%
0.076 51
 
3.3%
0.078 50
 
3.3%
0.084 49
 
3.2%
0.077 47
 
3.1%
0.082 44
 
2.9%
0.079 43
 
2.8%
0.071 43
 
2.8%
0.075 42
 
2.7%
Other values (142) 1041
68.1%
ValueCountFrequency (%)
0.012 2
 
0.1%
0.034 1
 
0.1%
0.038 2
 
0.1%
0.039 4
0.3%
0.041 4
0.3%
0.042 3
0.2%
0.043 1
 
0.1%
0.044 5
0.3%
0.045 4
0.3%
0.046 4
0.3%
ValueCountFrequency (%)
0.611 1
 
0.1%
0.61 1
 
0.1%
0.467 1
 
0.1%
0.464 1
 
0.1%
0.422 1
 
0.1%
0.415 3
0.2%
0.414 2
0.1%
0.413 1
 
0.1%
0.403 1
 
0.1%
0.401 1
 
0.1%

free sulfur dioxide
Real number (ℝ)

HIGH CORRELATION 

Distinct58
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.843586
Minimum1
Maximum72
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.9 KiB
2024-11-04T18:06:56.653522image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q17
median14
Q321
95-th percentile35
Maximum72
Range71
Interquartile range (IQR)14

Descriptive statistics

Standard deviation10.34505
Coefficient of variation (CV)0.65294873
Kurtosis1.9583288
Mean15.843586
Median Absolute Deviation (MAD)7
Skewness1.2162433
Sum24209
Variance107.02005
MonotonicityNot monotonic
2024-11-04T18:06:56.977377image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 126
 
8.2%
5 99
 
6.5%
15 77
 
5.0%
12 71
 
4.6%
10 69
 
4.5%
7 67
 
4.4%
9 61
 
4.0%
16 59
 
3.9%
11 58
 
3.8%
17 56
 
3.7%
Other values (48) 785
51.4%
ValueCountFrequency (%)
1 3
 
0.2%
2 1
 
0.1%
3 49
 
3.2%
4 40
 
2.6%
5 99
6.5%
5.5 1
 
0.1%
6 126
8.2%
7 67
4.4%
8 56
3.7%
9 61
4.0%
ValueCountFrequency (%)
72 1
 
0.1%
68 2
0.1%
66 1
 
0.1%
57 1
 
0.1%
53 1
 
0.1%
52 3
0.2%
51 4
0.3%
50 2
0.1%
48 2
0.1%
47 1
 
0.1%

total sulfur dioxide
Real number (ℝ)

HIGH CORRELATION 

Distinct144
Distinct (%)9.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.28534
Minimum6
Maximum289
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.9 KiB
2024-11-04T18:06:57.363641image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile11
Q122
median37
Q362
95-th percentile113
Maximum289
Range283
Interquartile range (IQR)40

Descriptive statistics

Standard deviation33.101253
Coefficient of variation (CV)0.7151563
Kurtosis3.910232
Mean46.28534
Median Absolute Deviation (MAD)18
Skewness1.5417693
Sum70724
Variance1095.6929
MonotonicityNot monotonic
2024-11-04T18:06:57.714021image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28 41
 
2.7%
15 34
 
2.2%
24 33
 
2.2%
18 33
 
2.2%
20 33
 
2.2%
31 32
 
2.1%
14 31
 
2.0%
38 30
 
2.0%
23 30
 
2.0%
19 29
 
1.9%
Other values (134) 1202
78.7%
ValueCountFrequency (%)
6 3
 
0.2%
7 4
 
0.3%
8 14
0.9%
9 14
0.9%
10 27
1.8%
11 26
1.7%
12 29
1.9%
13 28
1.8%
14 31
2.0%
15 34
2.2%
ValueCountFrequency (%)
289 1
0.1%
278 1
0.1%
165 1
0.1%
160 1
0.1%
155 1
0.1%
153 1
0.1%
152 1
0.1%
151 2
0.1%
149 1
0.1%
148 2
0.1%

density
Real number (ℝ)

HIGH CORRELATION 

Distinct414
Distinct (%)27.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.99656575
Minimum0.99007
Maximum1.001
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.9 KiB
2024-11-04T18:06:58.054661image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.99007
5-th percentile0.99358
Q10.99554
median0.99664
Q30.9976175
95-th percentile0.9993865
Maximum1.001
Range0.01093
Interquartile range (IQR)0.0020775

Descriptive statistics

Standard deviation0.0016850113
Coefficient of variation (CV)0.001690818
Kurtosis0.57389569
Mean0.99656575
Median Absolute Deviation (MAD)0.00106
Skewness-0.40700877
Sum1522.7525
Variance2.8392631 × 10-6
MonotonicityNot monotonic
2024-11-04T18:06:58.400250image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.9972 36
 
2.4%
0.9968 35
 
2.3%
0.9976 35
 
2.3%
0.998 29
 
1.9%
0.9962 28
 
1.8%
0.9978 26
 
1.7%
0.9964 25
 
1.6%
0.997 24
 
1.6%
0.9994 24
 
1.6%
0.9982 23
 
1.5%
Other values (404) 1243
81.3%
ValueCountFrequency (%)
0.99007 2
0.1%
0.9902 1
0.1%
0.99064 2
0.1%
0.9908 1
0.1%
0.99084 1
0.1%
0.9912 1
0.1%
0.9915 1
0.1%
0.99154 1
0.1%
0.99157 1
0.1%
0.9916 2
0.1%
ValueCountFrequency (%)
1.001 6
0.4%
1 10
0.7%
0.9999 1
 
0.1%
0.9998 10
0.7%
0.99976 1
 
0.1%
0.99975 1
 
0.1%
0.99974 1
 
0.1%
0.9997 8
0.5%
0.99965 1
 
0.1%
0.9996 12
0.8%

pH
Real number (ℝ)

HIGH CORRELATION 

Distinct87
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3169568
Minimum2.74
Maximum4.01
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.9 KiB
2024-11-04T18:06:58.657678image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2.74
5-th percentile3.08
Q13.22
median3.32
Q33.4
95-th percentile3.57
Maximum4.01
Range1.27
Interquartile range (IQR)0.18

Descriptive statistics

Standard deviation0.15190721
Coefficient of variation (CV)0.045797164
Kurtosis0.92150592
Mean3.3169568
Median Absolute Deviation (MAD)0.09
Skewness0.21457146
Sum5068.31
Variance0.023075802
MonotonicityNot monotonic
2024-11-04T18:06:59.050212image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.36 56
 
3.7%
3.3 54
 
3.5%
3.26 51
 
3.3%
3.39 48
 
3.1%
3.38 48
 
3.1%
3.29 45
 
2.9%
3.34 43
 
2.8%
3.28 41
 
2.7%
3.32 40
 
2.6%
3.35 39
 
2.6%
Other values (77) 1063
69.6%
ValueCountFrequency (%)
2.74 1
 
0.1%
2.87 1
 
0.1%
2.88 2
0.1%
2.89 4
0.3%
2.9 1
 
0.1%
2.92 1
 
0.1%
2.93 3
0.2%
2.94 4
0.3%
2.98 4
0.3%
2.99 2
0.1%
ValueCountFrequency (%)
4.01 2
0.1%
3.9 2
0.1%
3.85 1
 
0.1%
3.78 2
0.1%
3.75 1
 
0.1%
3.74 1
 
0.1%
3.72 3
0.2%
3.71 4
0.3%
3.7 1
 
0.1%
3.69 4
0.3%

sulphates
Real number (ℝ)

Distinct94
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6560733
Minimum0.33
Maximum2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.9 KiB
2024-11-04T18:06:59.414084image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.33
5-th percentile0.47
Q10.55
median0.62
Q30.7225
95-th percentile0.93
Maximum2
Range1.67
Interquartile range (IQR)0.1725

Descriptive statistics

Standard deviation0.17047151
Coefficient of variation (CV)0.25983608
Kurtosis12.083571
Mean0.6560733
Median Absolute Deviation (MAD)0.08
Skewness2.496377
Sum1002.48
Variance0.029060537
MonotonicityNot monotonic
2024-11-04T18:06:59.707843image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6 68
 
4.5%
0.58 67
 
4.4%
0.54 67
 
4.4%
0.62 61
 
4.0%
0.56 56
 
3.7%
0.57 54
 
3.5%
0.59 51
 
3.3%
0.55 50
 
3.3%
0.53 49
 
3.2%
0.63 48
 
3.1%
Other values (84) 957
62.6%
ValueCountFrequency (%)
0.33 1
 
0.1%
0.37 2
 
0.1%
0.39 6
 
0.4%
0.4 4
 
0.3%
0.42 5
 
0.3%
0.43 8
0.5%
0.44 14
0.9%
0.45 12
0.8%
0.46 18
1.2%
0.47 19
1.2%
ValueCountFrequency (%)
2 1
 
0.1%
1.98 1
 
0.1%
1.95 2
0.1%
1.62 1
 
0.1%
1.61 1
 
0.1%
1.59 1
 
0.1%
1.56 1
 
0.1%
1.36 3
0.2%
1.34 1
 
0.1%
1.33 1
 
0.1%

alcohol
Real number (ℝ)

Distinct59
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.433213
Minimum8.4
Maximum14.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.9 KiB
2024-11-04T18:07:00.074021image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum8.4
5-th percentile9.2
Q19.5
median10.2
Q311.1
95-th percentile12.5
Maximum14.9
Range6.5
Interquartile range (IQR)1.6

Descriptive statistics

Standard deviation1.0608213
Coefficient of variation (CV)0.10167733
Kurtosis0.1986475
Mean10.433213
Median Absolute Deviation (MAD)0.7
Skewness0.86148933
Sum15941.95
Variance1.1253418
MonotonicityNot monotonic
2024-11-04T18:07:00.415248image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.5 134
 
8.8%
9.4 101
 
6.6%
9.8 75
 
4.9%
10.5 67
 
4.4%
9.2 67
 
4.4%
10 62
 
4.1%
9.6 57
 
3.7%
9.3 56
 
3.7%
11 56
 
3.7%
9.7 51
 
3.3%
Other values (49) 802
52.5%
ValueCountFrequency (%)
8.4 1
 
0.1%
8.5 1
 
0.1%
8.7 2
 
0.1%
9 23
 
1.5%
9.05 1
 
0.1%
9.1 23
 
1.5%
9.2 67
4.4%
9.25 1
 
0.1%
9.3 56
3.7%
9.4 101
6.6%
ValueCountFrequency (%)
14.9 1
 
0.1%
14 7
0.5%
13.6 4
0.3%
13.5 1
 
0.1%
13.4 3
 
0.2%
13.3 3
 
0.2%
13.2 1
 
0.1%
13.1 2
 
0.1%
13 4
0.3%
12.9 9
0.6%

quality
Real number (ℝ)

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.6341623
Minimum3
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.9 KiB
2024-11-04T18:07:00.710983image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile5
Q15
median6
Q36
95-th percentile7
Maximum8
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.80863736
Coefficient of variation (CV)0.14352397
Kurtosis0.28817409
Mean5.6341623
Median Absolute Deviation (MAD)1
Skewness0.23770656
Sum8609
Variance0.65389439
MonotonicityNot monotonic
2024-11-04T18:07:00.960408image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
5 653
42.7%
6 607
39.7%
7 189
 
12.4%
4 52
 
3.4%
8 18
 
1.2%
3 9
 
0.6%
ValueCountFrequency (%)
3 9
 
0.6%
4 52
 
3.4%
5 653
42.7%
6 607
39.7%
7 189
 
12.4%
8 18
 
1.2%
ValueCountFrequency (%)
8 18
 
1.2%
7 189
 
12.4%
6 607
39.7%
5 653
42.7%
4 52
 
3.4%
3 9
 
0.6%

Interactions

2024-11-04T18:06:48.695005image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:05:58.745766image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:01.383253image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:04.207986image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:07.315066image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:12.848562image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:20.802068image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:26.821479image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:32.622955image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:37.859661image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:43.330052image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:45.917880image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:48.861403image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:05:58.928478image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:01.566885image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:04.550027image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:07.577044image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:13.304074image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:21.318874image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:27.254406image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:33.088158image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:38.123903image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:43.508783image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:46.145168image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:49.106603image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:05:59.095091image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:01.786476image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:04.782274image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:07.991242image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:13.825004image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:21.801630image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:27.762580image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:33.591269image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:38.609746image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:43.709339image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:46.377085image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:49.296958image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:05:59.300193image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:02.007817image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:05.046292image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:08.457873image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:14.335647image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:22.428780image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:28.235947image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:34.042824image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:39.049658image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:43.927666image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:46.601124image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:49.478710image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:05:59.595892image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:02.247380image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:05.322393image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:08.919855image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:14.837093image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:22.977584image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:28.781077image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:34.581497image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:39.505011image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:44.132732image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:46.810798image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:49.705604image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:05:59.795145image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:02.538551image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:05.579972image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:09.420480image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:15.287476image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:23.453507image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:29.311400image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:35.152192image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:39.970060image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:44.354145image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:47.004664image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:50.037235image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:00.031190image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:02.824235image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:05.833196image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:09.951770image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:15.876124image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:23.936167image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:29.808587image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:35.686813image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:40.538397image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:44.605504image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:47.224054image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:50.321245image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:00.244315image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:03.010614image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:06.089356image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:10.398436image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:16.486333image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:24.448343image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:30.255685image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:36.168079image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:41.023569image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:44.804905image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:47.478706image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:50.591183image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:00.419230image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:03.193838image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:06.385216image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:10.889736image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:18.879443image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:24.934683image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:30.720791image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:36.707402image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:41.547677image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:45.042675image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:47.728913image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:50.796927image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:00.643814image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:03.469728image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:06.613507image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:11.394698image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:19.311650image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:25.406986image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:31.193282image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:37.164361image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:41.963381image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:45.254118image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:47.977580image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:50.975657image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:00.853177image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:03.751402image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:06.836962image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:11.924439image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:19.854014image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:25.891836image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:31.683445image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:37.496332image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:42.537077image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:45.433591image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:48.210217image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:51.163243image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:01.115845image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:04.014904image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:07.089756image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:12.404198image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:20.352803image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:26.376865image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:32.161214image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:37.743860image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:42.999458image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:45.696151image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T18:06:48.524038image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-11-04T18:07:01.181624image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
alcoholchloridescitric aciddensityfixed acidityfree sulfur dioxidepHqualityresidual sugarsulphatestotal sulfur dioxidevolatile acidity
alcohol1.000-0.2960.106-0.482-0.058-0.0800.1740.4890.1290.213-0.268-0.241
chlorides-0.2961.0000.0940.4050.235-0.010-0.225-0.1880.1930.0090.1250.158
citric acid0.1060.0941.0000.3110.642-0.060-0.5290.2200.1480.3370.018-0.622
density-0.4820.4050.3111.0000.584-0.041-0.275-0.1930.3800.1460.1280.040
fixed acidity-0.0580.2350.6420.5841.000-0.173-0.6970.1180.1780.203-0.094-0.275
free sulfur dioxide-0.080-0.010-0.060-0.041-0.1731.0000.105-0.0550.0690.0350.7900.010
pH0.174-0.225-0.529-0.275-0.6970.1051.000-0.044-0.062-0.074-0.0180.230
quality0.489-0.1880.220-0.1930.118-0.055-0.0441.0000.0320.378-0.207-0.390
residual sugar0.1290.1930.1480.3800.1780.069-0.0620.0321.0000.0120.1310.033
sulphates0.2130.0090.3370.1460.2030.035-0.0740.3780.0121.000-0.016-0.337
total sulfur dioxide-0.2680.1250.0180.128-0.0940.790-0.018-0.2070.131-0.0161.0000.085
volatile acidity-0.2410.158-0.6220.040-0.2750.0100.230-0.3900.033-0.3370.0851.000

Missing values

2024-11-04T18:06:51.498693image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-04T18:06:51.887647image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

fixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholquality
16327.40.700.001.90.07611.034.00.99783.510.569.45
16337.80.880.002.60.09825.067.00.99683.200.689.85
16347.80.760.042.30.09215.054.00.99703.260.659.85
163511.20.280.561.90.07517.060.00.99803.160.589.86
16367.40.700.001.90.07611.034.00.99783.510.569.45
16377.40.660.001.80.07513.040.00.99783.510.569.45
16387.90.600.061.60.06915.059.00.99643.300.469.45
16397.30.650.001.20.06515.021.00.99463.390.4710.07
16407.80.580.022.00.0739.018.00.99683.360.579.57
16417.50.500.366.10.07117.0102.00.99783.350.8010.55
fixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholquality
32216.60.7250.207.80.07329.079.00.997703.290.549.25
32226.30.5500.151.80.07726.035.00.993143.320.8211.66
32235.40.7400.091.70.08916.026.00.994023.670.5611.66
32246.30.5100.132.30.07629.040.00.995743.420.7511.06
32256.80.6200.081.90.06828.038.00.996513.420.829.56
32266.20.6000.082.00.09032.044.00.994903.450.5810.55
32275.90.5500.102.20.06239.051.00.995123.520.7611.26
32286.30.5100.132.30.07629.040.00.995743.420.7511.06
32295.90.6450.122.00.07532.044.00.995473.570.7110.25
32306.00.3100.473.60.06718.042.00.995493.390.6611.06

Duplicate rows

Most frequently occurring

fixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholquality# duplicates
226.70.4600.241.70.07718.034.00.994803.390.6010.664
527.20.3600.462.10.07424.044.00.995343.400.8511.074
637.20.6950.132.00.07612.020.00.995463.290.5410.154
817.50.5100.021.70.08413.031.00.995383.360.5410.564
56.00.5000.001.40.05715.026.00.994483.360.459.553
126.40.6400.211.80.08114.031.00.996893.590.669.853
397.00.6500.022.10.0668.025.00.997203.470.679.563
407.00.6900.072.50.09115.021.00.995723.380.6011.363
607.20.6300.001.90.09714.038.00.996753.370.589.063
1047.80.6000.262.00.08031.0131.00.996223.210.529.953